Levy M. James

The return of thalidomide: Are birth defects surveillance systems ready?

In the 1960s, thalidomide caused limb deficiencies in thousands of infants worldwide. The limb deficiencies were frequently of the intercalary type. As a result, numerous countries started birth defect surveillance programs. In 1967, the Centers for Disease Control (CDC) started the Metropolitan Atlanta Congenital Defects Program (MACDP), a population-based surveillance system, to provide early warning against new teratogens. Recent studies have shown that thalidomide may be beneficial for a range of conditions, including cancer and AIDS, and it may once again become widely available. Here, we examine the ability of MACDP to detect an increase in the birth prevalence of limb deficiency as an early warning of fetal exposure to thalidomide. We calculated base rates for all limb deficiencies, for bilateral nonsyndromic intercalary or preaxial deficiencies, and for all nonsyndromic intercalary limb deficiencies among Atlanta infants born from 1968 through 1993. We used relative risk estimates from previous studies and a range of pregnancy exposure rates for thalidomide. We tested the statistical power of MACDP to detect subtle changes in the birth prevalence of these defects using Poisson and cumulative sum (CUSUM) techniques. The base rates for all limb deficiencies, for bilateral intercalary or preaxial deficiencies, and for all intercalary limb deficiencies, were 0.53, 0.035, and 0.022/1,000, and the estimated relative risks were 175, 4,570, and 8,180, respectively. We varied the assumed rate of exposure to thalidomide from 1/10,000 to 5/100. With a 1/1,000 exposure rate, both Poisson and CUSUM techniques will detect a rate change in intercalary limb deficiency in about 6 months of monitoring, and a rate change in bilateral intercalary or preaxial deficiencies in about 12 months of monitoring. When monitoring all limb deficiencies, a pregnancy exposure rate of 3.5% or less would go unnoticed by the Poisson method and would take more than 50 years for the CUSUM method to signal an alarm with a 1/1,000 exposure rate. However, for rates of exposure less than 1/1,000, a progressively longer period of time or larger sample are needed to detect a rate change by both methods. Our findings highlight the importance of enlarging the monitored population and correct case classification in birth defects surveillance.

Population-based birth-defect and risk-factor surveillance: data from the Northern Netherlands.

In many countries, birth defect monitoring systems have been set up in order to identify new teratogens as soon as possible. The usual approach to monitoring involves analysis of the frequency of specific birth defects over time. This approach has been criticized as having poor statistical power to detect epidemics due to new rare teratogenic exposures. A proposed alternative approach is the on-going analysis of risk-factor data with a case-control approach. In this paper, we present birth-defects and risk-factor surveillance data from the Northern Netherlands (NNL). For years of birth 1981-1994, 4014 cases had been registered. We investigated combinations of 32 diagnostic categories and 77 risk factors. For 10 combinations a P value < 0.01 was found; for another 25, the P value was between 0.01 and 0.05. We then checked these positive associations against data from the Metropolitan Atlanta Congenital Defects Program (MACDP) and the MAternal DRug Exposure surveillance project (MADRE). In all three data sets, an association between maternal use of psychotropic drugs (psycholeptics) and cleft lip with or without cleft palate (CLP) was present. The highest odds ratio was found for CLP and maternal use of oxazepam in the NNL data (OR = 8.17, 95% CI 1.26-42.2). Both in the MACDP data and in the NNL data, an association between maternal smoking and clubfoot was found. Although the odds ratios were low, the attributable fraction derived from the NNL data was 11%. Methodologic issues that should be considered in this approach include exposure ascertainment and classification, outcome specificity, and type I errors. The strengths of this approach include its population-based nature and the ability of users to check results against results from other similar systems.